Finite element modeling and analysis method for soft tissue stress of sacrococcygeal region

Abstract

The invention provides a finite element modeling and analysis method for sacrococcygeal region soft tissue stress, and relates to the technical field of sacrococcygeal region soft tissue stress analysis. Three-dimensional image data of normal sacrococcygeal region soft tissue are collected, and an image segmentation technology is utilized to accurately extract a soft tissue contour; constructing a three-dimensional surface dynamic model based on the extracted soft tissue contour; simulating the deformation attribute of the three-dimensional surface dynamic model when the three-dimensional surface dynamic model is stressed; and measuring the deformation attribute of the soft tissue to be measured when the soft tissue is stressed, comparing the deformation attribute of the soft tissue to be measured when the soft tissue is stressed with the deformation attribute of the three-dimensional surface dynamic model when the three-dimensional surface dynamic model is stressed, and evaluating the supporting force of the soft tissue to be measured.

Description

Technical Field

[0001] The present invention provides a finite element modeling and analysis method for sacrococcygeal soft tissue stress, and relates to the technical field of sacrococcygeal soft tissue stress. Background Art

[0002] The sacrum and coccyx are a high-incidence area of ​​human soft tissue damage, and the study of their mechanical properties has important clinical value and practical significance. For patients who are bedridden for a long time, the sacrum and coccyx are under continuous pressure. People who sit for a long time are also at risk of chronic soft tissue injury because the sacrum and coccyx are in a non-physiological stress state for a long time. In orthopedic clinical scenarios, the mechanical environment of soft tissue changes significantly after sacral fractures, tumor resections, and orthopedic surgical interventions. Accurate force reconstruction is required to assess healing risks and guide the formulation of rehabilitation plans. However, traditional empirical evaluation methods have difficulty quantifying the pressure-deformation relationship of soft tissue under complex loads and cannot meet the needs of personalized medical care. Therefore, there is an urgent need to use finite element analysis technology to reveal its inherent mechanical mechanism.

[0003] In the early days of the development of finite element analysis technology, sacral soft tissue modeling mostly used simplified geometric models, that is, abstracting complex anatomical structures into regular shapes. Although this simplified method can reduce computational complexity, it seriously deviates from the true anatomical morphology, resulting in a large deviation between the pressure distribution simulation results and the actual situation, and cannot accurately reflect the mechanical response of soft tissue in different postures and support conditions. In addition, traditional solid finite element models face the problems of a large number of grids and low computational efficiency. A single simulation often takes hours or even longer, which makes it difficult to meet the needs of rapid clinical evaluation and parameter optimization. At the same time, traditional models have deficiencies in the characterization of material properties and cannot effectively describe complex mechanical properties such as the hyperelasticity of soft tissue and the anisotropy of muscle tissue, which limits the accuracy of the model's simulation of actual mechanical behavior. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a finite element modeling and analysis method for the stress of sacral soft tissue, including the following steps: collecting three-dimensional image data of normal sacral soft tissue and extracting the soft tissue contour; constructing a three-dimensional surface dynamic model based on the extracted soft tissue contour; simulating the stress conditions of the three-dimensional surface dynamic model and calculating the deformation properties of the three-dimensional surface dynamic model; measuring the deformation properties of the soft tissue to be tested when it is subjected to stress, and comparing the deformation properties of the soft tissue to be tested when it is subjected to stress with the deformation properties of the three-dimensional surface dynamic model when it is subjected to stress, to evaluate the support force recovery of the soft tissue to be tested.

[0005] In a preferred embodiment, a three-dimensional surface mesh is generated based on the extracted soft tissue contour using a triangular patch meshing method; a structural sub-model, a bending sub-model, and a dissipative sub-model are constructed between adjacent vertices of the three-dimensional surface mesh to convert the three-dimensional surface mesh into a three-dimensional surface dynamic model.

[0006] In a preferred embodiment, a structural sub-model is built between adjacent vertices of a three-dimensional surface mesh, and the original length of the edge between adjacent vertices i and j is set to , the actual length between adjacent vertices i and j is , then the resistance F output by the structural sub-model is ij for:

[0007] ;

[0008] in, is the compressive strength coefficient, P i 、P j Represents the coordinate vector of vertices i and j in space.

[0009] In a preferred embodiment, a curved sub-model is constructed between two non-shared vertices of two adjacent triangular facets in a three-dimensional surface mesh:

[0010] Suppose two adjacent triangles (i,j,m) and The initial two-face segment of (i,j,n) and The angle is , the actual angle after the current bending force is , then the bending sub-model between the two unshared vertices m and n will bend moment Converted into equivalent pulling force :

[0011] ;

[0012] in, is the bending coefficient.

[0013] In a preferred embodiment, a dissipative sub-model is constructed in parallel with the structural sub-model between the same vertex pairs i and j as each structural sub-model, and the dissipative force output by the dissipative sub-model is for:

[0014]

[0015] in, is the velocity of vertex i, is the velocity of vertex j, c d is the dissipation coefficient.

[0016] In a preferred embodiment, the dynamic response model of each vertex is constructed as follows:

[0017] ;

[0018] Among them, NA(i) is the set of vertices connected to vertex i through the structural sub-model; NB(i) is the set of vertices connected to vertex i through the bending sub-model; ND(i) is the set of vertices connected to vertex i through the dissipative sub-model; F ei is the external load on vertex i, m i is the mass of vertex i;

[0019] By combining the equations of all vertices, we can obtain the three-dimensional surface dynamic model:

[0020] ;

[0021] Where M is a diagonal matrix with m diagonal elements. i , K is the force matrix, C is the dissipation matrix, P is the position vector array of all vertices, F e is the external load array.

[0022] In a preferred embodiment, the range of the collision body is calculated, the grid cells of the collision surface of the collision body are marked as active grids, all active grids are traversed, and the vertices in the active grids are collected to form a candidate set R; the position vector of each candidate vertex r is substituted into the surface equation of the three-dimensional surface dynamic model, and the function value is output:

[0023] Output the position vector of the candidate vertex r at the previous moment The function value f old =f( );

[0024] Output candidate vertex r current position vector The function value f new =f( );

[0025] Determine whether the candidate vertex r is on the surface or inside the three-dimensional surface dynamic model:

[0026] If f old ≥0 and f new <0, the candidate vertex r is considered as a collision point; if , consider the candidate vertex r as a potential collision point, is the distance threshold.

[0027] In a preferred embodiment, the detected collision points and potential collision points constitute a collision area. For the three-dimensional surface dynamic model, the surface points located in the collision area before the collision are set as deformation points, and the deformation properties of the deformation points after being subjected to force are calculated. The deformation properties of multiple deformation points after being subjected to force constitute a theoretical deformation property cloud map; the deformation properties of the corresponding deformation points of the soft tissue to be tested are measured after being subjected to the same force to form a measured deformation property cloud map, and the measured deformation property cloud map is compared with the theoretical deformation property cloud map to analyze the differences in the deformation properties of the soft tissue to be tested, and the degree of recovery of the supporting force of the soft tissue to be tested is evaluated based on the differences in the deformation properties.

[0028] In a preferred embodiment, after the pressure is released, the position vector of the surface deformation point J of the three-dimensional surface dynamic model at time t is Substitute the surface equation and output the function value of the deformation point J at time t ;

[0029] Record the deformation depth of deformation point J at consecutive time steps after the pressure is released:

[0030] ;

[0031] ;

[0032] Calculate the deformation point J through Deformation recovery rate over time:

[0033] ;

[0034] Calculate the deformation point J at the same position at time t after the soft tissue under test is relieved of the same pressure. Deformation recovery rate over time , select Q deformation points J to calculate the restoration evaluation value z:

[0035] ;

[0036] The degree of soft tissue support recovery is quantified by the recovery assessment value z.

[0037] In a preferred embodiment, MRI elastic imaging technology is used to set an excitation frequency that matches the shear wave propagation characteristics of soft tissue, collect echo signals of the soft tissue to be measured, and extract deformation properties.

[0038] Compared with the prior art, the present invention has the following beneficial technical effects:

[0039] 1. Extract the true soft tissue contour through medical image segmentation, build a surface dynamic response model, and eliminate the deviation caused by traditional simplified geometric models.

[0040] 2. Synchronously measure the soft tissue deformation data, compare it with the simulation results in multiple parameters, and quantitatively evaluate the support force to establish a mapping relationship between the deformation property differences and the support function.

[0041] 3. Lightweight surface mesh modeling: Compared with traditional solid finite element models, the number of meshes in the surface dynamic response model is reduced, and the time required for a single simulation is shortened from hours to minutes.

[0042] 4. Through the structural sub-model, bending sub-model, and dissipative sub-model architecture, the soft tissue properties are fully characterized, and high-risk areas are output through support force assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 Schematic diagram of the process of the finite element modeling and analysis method of the sacral soft tissue stress of the present invention;

[0045] Figure 2 is a schematic diagram of a three-dimensional surface mesh;

[0046] Figure 3 Construct schematic diagrams for the structural sub-model, bending sub-model, and dissipative sub-model;

[0047] Figure 4 The deformation depth h-time t variation curves of the three deformation points;

[0048] Figure 5 The finite element modeling method of the present invention is compared with the traditional technical data. DETAILED DESCRIPTION

[0049] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] The stress analysis of sacrococcygeal soft tissue is of great significance in clinical medicine and biomechanical research. Accurate modeling methods can provide a key basis for auxiliary diagnosis of sacrococcygeal diseases.

[0051] Example 1

[0052] The present invention proposes a finite element modeling and analysis method for the stress of sacrococcygeal soft tissue. Figure 1 The specific steps are as follows:

[0053] S1. Collect three-dimensional image data of normal sacrococcygeal soft tissue and extract the contour of sacrococcygeal soft tissue.

[0054] CT scans are used to obtain 3D imaging data of the sacral soft tissue. Image segmentation techniques are used to accurately extract soft tissue contours and separate different tissue layers, such as skin, muscle, and fat. Noise reduction and smoothing are performed on the segmented images to provide high-precision geometric data for subsequent modeling.

[0055] S2. Construct a three-dimensional surface dynamic model based on the extracted soft tissue contours.

[0056] S21. Based on the extracted soft tissue contour, a meshing method is used to generate a three-dimensional surface mesh.

[0057] In this embodiment, a triangular patch meshing method is used to generate a three-dimensional surface mesh, such as Figure 2 As shown in Figure 2, triangular facets are the basic unit for constructing soft tissue surface models. They can split complex tissue shapes into a large number of triangular faces to facilitate mathematical modeling and mechanical calculations.

[0058] The triangular patch meshing method ensures that the circumscribed circle of any triangular patch does not contain any vertices other than the three vertices of the triangular patch, and the mesh vertices evenly cover the surface contour.

[0059] The vertex coordinate set output after triangular mesh subdivision and triangle face index set ; Where N is the total number of vertices, t is the vertex number, M is the total number of triangles, k is the triangle number, and i, j, and m are the three vertices of the kth triangle.

[0060] S22. Build a structural sub-model, a bending sub-model, and a dissipative sub-model between adjacent vertices of the three-dimensional surface mesh to convert the three-dimensional surface mesh into a three-dimensional surface dynamic model.

[0061] The structural sub-model is used to maintain the original distance between vertices and simulate the tensile stiffness of the tissue; the bending sub-model is used to resist the angle change between adjacent triangular facets and reflect the bending characteristics of the tissue; the dissipative sub-model simulates the viscoelastic behavior of the tissue, such as Figure 3 shown.

[0062] (1) Build a structural sub-model between adjacent vertices of the three-dimensional surface mesh.

[0063] Assume that the original length of the edge between two vertices i and j in the structural submodel is , the original length is the length of the line connecting the two vertices in the natural state; the actual length between the two vertices i and j is , the actual length is the length of the line connecting the two vertices when under stress, so the resistance F output by the structural sub-model is ij for:

[0064] ;

[0065] Resistance F output by the structural sub-model ij It reflects the mechanical response of tissues to pressure. P is the compressive coefficient, which reflects the ability of the tissue to resist deformation under pressure; i 、P j Represents the coordinate vectors of two vertices i and j in space.

[0066] (2) Construct a curved sub-model between two non-shared vertices of two adjacent triangular facets in a three-dimensional surface mesh.

[0067] Suppose two adjacent triangles (i,j,m) and The initial angle between the two faces of (i, j, n) is , the actual angle after the current bending force is , then the bending sub-model between the two unshared vertices m and n will bend moment Converted into equivalent pulling force :

[0068] ;

[0069] The curvature coefficient controls the curvature of the 3D surface mesh.

[0070] (3) In parallel with the structural sub-model, a dissipation sub-model is added to the same vertex pair i and j of each structural sub-model. The dissipation force output by the dissipation sub-model is Proportional to the vertex relative velocity:

[0071] ;

[0072] in, is the relative velocity between vertices i and j, is the velocity of vertex i, is the velocity of vertex j, c d is the dissipation coefficient, which is used to simulate the tissue viscous resistance.

[0073] (4) Construct the dynamic response model of each vertex as follows:

[0074] ;

[0075] Where: NA(i) is the set of vertices connected to vertex i through the structural sub-model; NB(i) is the set of vertices connected to vertex i through the bending sub-model; ND(i) is the set of vertices connected to vertex i through the dissipative sub-model; F ei is the external load on the soft tissue, m i is the mass of vertex i.

[0076] By combining the equations of all vertices, we can obtain a three-dimensional surface dynamic model with vertex displacement, velocity, and acceleration as unknown quantities. The three-dimensional surface dynamic model is expressed in matrix form as follows:

[0077] ;

[0078] Where M is a diagonal matrix with m diagonal elements. i ,K is the force matrix, which is constructed by the resistance and equivalent force output by the structure and bending sub-models. C is constructed by the dissipation force and the dissipation coefficient. P is the position vector array of all vertices. F e is the external load array.

[0079] S3. Simulate the deformation properties of the surface vertices of a three-dimensional surface dynamic model when subjected to force.

[0080] Simulate the stress conditions of three-dimensional surface dynamic models and calculate the deformation properties of surface vertices when subjected to stress.

[0081] Let the position vector of vertex i at the previous time step be , the position vector of vertex i at the current time step is , speed is V i .

[0082] Construct the surface equation f(x,y,z)=ax+by+cz+d of the three-dimensional surface dynamic model.

[0083] Among them, (x, y, z) are the surface vertex coordinates of the three-dimensional surface dynamic model, (a, b, c) are the surface normal vector component parameters, the distance constant term is d, and the normal vector component determines the orientation of the contact surface in space. For example, when the normal vector component parameter is (1, 0, 0), the contact surface is perpendicular to the x-axis direction.

[0084] Calculate the minimum and maximum values ​​of the collision body on the three-dimensional coordinate axis (i.e. x min ,x max ,y min ,y max ,z min ,z max ), and get the range of the collision body based on these coordinates.

[0085] The grid cells of the collision surface of the collider are marked as active grids, all active grids are traversed, and the vertices in the active grids are collected to form a candidate set R.

[0086] For each candidate vertex r∈R, perform the following operations:

[0087] Substitute the position vector of each candidate vertex r into the surface equation and output the function value:

[0088] Output the position vector of the candidate vertex r at the previous moment The function value f old =f( );

[0089] Output candidate vertex r current position vector The function value f new =f( );

[0090] Determine whether the candidate vertex r is on the surface or inside the three-dimensional surface dynamic model:

[0091] If f old ≥0 and f new <0, the candidate vertex r is considered as a collision point, that is, the vertex enters the interior from the surface.

[0092] like , considered as close to the surface, the candidate vertex r is considered as a potential collision point, is the distance threshold.

[0093] The detected collision points and potential collision points constitute the collision area. For the three-dimensional surface dynamic model, the surface points located in the collision area before the collision are set as deformation points, and the deformation properties of the deformation points after being subjected to force are calculated.

[0094] The deformation properties of multiple deformation points after being subjected to force constitute the theoretical deformation property cloud map.

[0095] S4. Measure the deformation properties of the soft tissue under test when it is subjected to stress, and compare the deformation properties of the soft tissue under test when it is subjected to stress with the theoretical deformation properties of the three-dimensional surface dynamic model under stress to evaluate the support force recovery of the soft tissue under test.

[0096] MRI elastic imaging technology is used to non-invasively detect internal deformation of soft tissue and simultaneously collect mechanical data.

[0097] For soft tissue, the excitation frequency is set to match the shear wave propagation characteristics of soft tissue, preferably 100-300 Hz. According to the thickness of the soft tissue, the encoding gradient intensity and duration are adjusted so that the shear wave covers the entire tissue layer and clearly captures the shear deformation transmission between soft tissues.

[0098] When MRI acquires echo signals, shear waves propagate after soft tissue is stressed, causing tissue particles to displace, destroying the original phase consistency of the proton group. Phase changes are converted into displacement field data of pixel-level displacement vector distribution through phase difference algorithms such as complex autocorrelation analysis; then, through the strain calculation module based on displacement field gradient operation, strain fields of shear strain and normal strain distribution are generated to quantify the spatial differences in internal deformation between damaged and healthy areas; and the corresponding data of tissue deformation are recorded simultaneously, preferably recording the deformation depth-time change curve, such as Figure 4 As shown, the deformation depth h-time t change curves of the three deformation points after the pressure is released are exemplarily shown, where h is a normalized value.

[0099] The collected mechanical data and deformation data are imported into DIC analysis software to extract deformation properties. Preferably, the deformation properties include the deformation depth distribution at each point, the gradient of deformation direction change, and the change of deformation recovery rate in different time dimensions. Finally, a visual cloud map of the deformation properties of the soft tissue to be tested is generated to intuitively present the deformation characteristics of the soft tissue.

[0100] The deformation property cloud map of the soft tissue to be tested is compared with the theoretical deformation property cloud map of the three-dimensional surface dynamic model in the collision area to intuitively present the difference in deformation properties between the two and locate the abnormal support force parts. In practical applications, the location of abnormal support force parts mainly includes: the coordinated deformation of healthy tissue in the model, the abrupt strain in the soft tissue damage repair area to be tested, and the prompt that the support force in this area has not been fully restored, thereby realizing the evaluation of the recovery degree of soft tissue support force and problem identification.

[0101] Table 1 Comparison of finite element parameters

[0102]

[0103] Table 2 Comparison of mechanical properties of models

[0104]

[0105] According to the comparison data in Table 1 and Table 2, the finite element modeling method of the present invention has achieved a breakthrough in computational efficiency compared with the traditional technology. Figure 5 As shown in the figure, the number of grid elements was reduced by 81.7%, achieving model lightweighting; the simulation time was reduced from 4.3 hours to 6.5 minutes, and the simulation speed was accelerated by 39.7 times; the mechanical accuracy was improved, the strain analysis error was reduced by 78.8%, the energy hysteresis rate fit R² was increased to 0.94, the curvature prediction error was reduced by 71.2%, and the dissipation calibration error was reduced by 76.8%.

[0106] Example 2

[0107] Quantitatively evaluate the deformation properties of the soft tissue under test and the deformation properties of the three-dimensional surface dynamic model.

[0108] Substitute the position vector of the surface deformation point J of the three-dimensional surface dynamic model located in the collision area into the surface equation and output the function value of the surface deformation point J at time t ;

[0109] Record the deformation depth of the surface deformation point J at consecutive time steps after the pressure is released:

[0110] ;

[0111] ;

[0112] Calculate the deformation point J through Deformation recovery rate over time:

[0113] .

[0114] By using the measurement method of step S4 in Example 1, the deformation point J of the soft tissue to be tested is calculated after the same pressure is removed at time t. Deformation recovery rate over time , a total of Q deformation points J are selected to calculate the restoration evaluation value z:

[0115] ;

[0116] The degree of soft tissue support recovery was quantified by the quantitative recovery assessment value z.

[0117] Preferably, the degree of recovery of the soft tissue support force is divided into complete recovery, basic recovery, and poor recovery.

[0118] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. The database involved in the embodiments provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on a regional block chain, etc., but is not limited to this. The processor involved in the embodiments provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited to this.

[0119] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A finite element modeling and analysis method for sacrococcygeal soft tissue stress, characterized in that: The following steps are involved: Collect three-dimensional image data of normal sacral soft tissue and extract the soft tissue contour; construct a three-dimensional surface dynamic model based on the extracted soft tissue contour; simulate the stress conditions of the three-dimensional surface dynamic model and calculate the deformation properties of the three-dimensional surface dynamic model; measure the deformation properties of the soft tissue to be tested when subjected to stress, and compare the deformation properties of the soft tissue to be tested when subjected to stress with the deformation properties of the three-dimensional surface dynamic model when subjected to stress to evaluate the support force recovery of the soft tissue to be tested.

2. The finite element modeling and analysis method for sacrococcygeal soft tissue stress according to claim 1, characterized in that: Based on the extracted soft tissue contour, a three-dimensional surface mesh is generated using a triangular patch meshing method. A structural sub-model, a bending sub-model, and a dissipative sub-model are constructed between adjacent vertices of the three-dimensional surface mesh to convert the three-dimensional surface mesh into a three-dimensional surface dynamic model.

3. The finite element modeling and analysis method for sacrococcygeal soft tissue stress according to claim 2, characterized in that: Build a structural sub-model between adjacent vertices of the 3D surface mesh, and assume that the original length of the edge between adjacent vertices i and j is , the actual length between adjacent vertices i and j is , then the resistance F output by the structural sub-model is ij for: ; in, is the compressive strength coefficient, P i 、P j Represents the coordinate vector of vertices i and j in space.

4. The finite element modeling and analysis method for sacrococcygeal soft tissue stress according to claim 3, characterized in that: Construct a curved sub-model between two non-shared vertices of two adjacent triangles in a 3D surface mesh: Suppose two adjacent triangles (i,j,m) and The initial two-face segment of (i,j,n) and The angle is , the actual angle after the current bending force is , then the bending sub-model between the two unshared vertices m and n will bend moment Converted into equivalent pulling force : ； in, is the bending coefficient.

5. The finite element modeling and analysis method for sacrococcygeal soft tissue stress according to claim 4, characterized in that: Between the same vertex pairs i and j as each structural sub-model, a dissipative sub-model is constructed in parallel with the structural sub-model. The dissipative force output by the dissipative sub-model is for: in, is the velocity of vertex i, is the velocity of vertex j, c d is the dissipation coefficient.

6. The finite element modeling and analysis method for sacrococcygeal soft tissue stress according to claim 5, characterized in that: The dynamic response model of each vertex is constructed as: ； Among them, NA(i) is the set of vertices connected to vertex i through the structural sub-model; NB(i) is the set of vertices connected to vertex i through the bending sub-model; ND(i) is the set of vertices connected to vertex i through the dissipative sub-model; F ei is the external load on vertex i, m i is the mass of vertex i; By combining the equations of all vertices, we can obtain the three-dimensional surface dynamic model: ； Where M is a diagonal matrix with m diagonal elements. i , K is the force matrix, C is the dissipation matrix, P is the position vector array of all vertices, F e is the external load array.

7. The finite element modeling and analysis method for sacrococcygeal soft tissue stress according to claim 1, characterized in that: Calculate the range of the collision body, mark the grid cells of the collision surface of the collision body as active grids, traverse all active grids, collect the vertices in the active grids, and form a candidate set R; bring the position vector of each candidate vertex r into the surface equation of the three-dimensional surface dynamic model and output the function value: Output the position vector of the candidate vertex r at the previous moment The function value f old =f( ); Output candidate vertex r current position vector The function value f new =f( ); Determine whether the candidate vertex r is on the surface or inside the three-dimensional surface dynamic model: If f old ≥0 and f new <0, the candidate vertex r is considered as a collision point; if , consider the candidate vertex r as a potential collision point, is the distance threshold.

8. The finite element modeling and analysis method for sacrococcygeal soft tissue stress according to claim 7, characterized in that: The detected collision points and potential collision points constitute the collision area. For the three-dimensional surface dynamic model, the surface points located in the collision area before the collision are set as deformation points, and the deformation properties of the deformation points after being subjected to force are calculated. The deformation properties of multiple deformation points after being subjected to force constitute a theoretical deformation property cloud map; the deformation properties of the corresponding deformation points of the soft tissue to be tested are measured after being subjected to the same force to form a measured deformation property cloud map. The measured deformation property cloud map is compared with the theoretical deformation property cloud map, and the differences in the deformation properties of the soft tissue to be tested are analyzed. Based on the differences in the deformation properties, the degree of recovery of the support force of the soft tissue to be tested is evaluated.

9. The finite element modeling and analysis method for sacrococcygeal soft tissue stress according to claim 7, characterized in that: After the pressure is released, the position vector of the surface deformation point J of the three-dimensional surface dynamic model at time t is Substitute the surface equation and output the function value of the deformation point J at time t ; Record the deformation depth of deformation point J at consecutive time steps after the pressure is released: ； ； Calculate the deformation point J through Deformation recovery rate over time: ； Calculate the deformation point J at the same position at time t after the soft tissue under test is relieved of the same pressure. Deformation recovery rate over time , select Q deformation points J to calculate the restoration evaluation value z: ； The degree of soft tissue support recovery is quantified by the recovery assessment value z.

10. The finite element modeling and analysis method for sacrococcygeal soft tissue stress according to claim 1, characterized in that: Using MRI elastic imaging technology, the excitation frequency is set to match the shear wave propagation characteristics of soft tissue, the echo signal of the soft tissue to be tested is collected, and the deformation properties are extracted.

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